Abstract: In Radar Automatic Target Recognition （RATR）， data-driven models have proven to be a powerful tool. However， the performance of the data-driven models were dependent on the quality of the data set. The data enhancement method could improve the recognition performance of the data-driven models on the existing data set by expanding the data set. This paper proposes a one-dimensional basic generative adversarial network （BGAN） structure and a conditional generative adversarial network （CGAN） structure for high resolution range profile （HRRP） data generation. Then using the generated artificial samples to complete the data enhancement. Experiments show that the two networks proposed in this paper can effectively improve the accuracy of target recognition， and the performance is better than the traditional translation and mirroring enhancement methods. The BGAN-based HRRP data enhancement method has the best performance， but its time and space complexity are relatively high； the CGAN-based data enhancement method can reduce the time and space complexity of the model while ensuring the increase in accuracy， and has high application prospects.